Fraudsters are getting smarter — and faster. With generative AI and deepfake technology at their fingertips, they're flooding insurers with fake claims and exposing cracks in traditional fraud detection methods. Insurers are in a high-stakes race against AI-powered deception, and the cost of falling behind is steep: billions in losses and eroded customer trust.

Outdated systems can't keep up with AI-driven scams. It's no longer a question of if insurers should adopt AI-powered fraud detection but how fast they can do it. The good news is the same technology that fuels fraudulent claims can also be used to fight them.

Generative AI offers insurers a way to not only detect and combat fraud but also streamline case management, accelerating claims processing and improving efficiency.

The Game Changer in Claims Management

Insurance fraud isn't a new problem, but it's never been this sophisticated. Gone are the days when fraudulent claims were limited to exaggerated injuries and staged accidents. Today's fraudsters have access to AI-generated medical records, synthetic identities, and eerily convincing deepfake videos, allowing them to construct entirely fabricated incidents with alarming precision.

Traditional fraud detection methods — document reviews, phone interviews, and outdated rule-based systems — are no match for the scale and speed at which AI-powered fraud is evolving. But with the ability to process billions of data points in real time, AI-powered fraud detection systems can do what human analysts cannot: instantly cross-reference claims against vast datasets, identify inconsistencies, and flag suspicious activity before payouts occur. This technology enables insurers to detect deepfake-generated documents and videos, analyze behavioral patterns that suggest fraudulent intent, and shut down scams before they drain company resources.

Unlike legacy systems that react to fraud only after it has occurred, AI-driven fraud detection is predictive and preventative. By leveraging machine learning models trained on historical fraud cases, insurers can anticipate emerging fraud tactics, staying one step ahead of the criminals. This shift from reactive to proactive fraud prevention is a game-changer, saving insurers billions while safeguarding legitimate policyholders.

How AI Automates and Accelerates Claims Processing

Fraud detection is only half the battle. Insurers are also under pressure to process legitimate claims quickly and accurately. Customers expect seamless, hassle-free settlements, and insurers that fail to deliver risk damaging their reputation. Generative AI not only combats fraud but also revolutionizes claims processing, allowing insurers to operate with unprecedented speed and efficiency.

One of the most significant advantages of AI is automation in the records retrieval process. Traditionally, insurers relied on manual verification processes, which involved requesting medical records, police reports, and other supporting documents. AI-powered claims processing can help limit the need for time-consuming manual labor by instantly verifying, retrieving, and analyzing records from multiple sources.

Natural language processing (NLP) further enhances claims processing by extracting key insights from medical records, adjuster notes, and even policyholder communications. This allows insurers to assess the legitimacy of claims with remarkable accuracy, ensuring that genuine cases are settled swiftly while fraudulent ones are flagged for further investigation. Moreover, AI-generated summaries provide claims adjusters with clear, concise insights, minimizing the need for extensive document review. By scanning vast amounts of structured and unstructured data, including text, images, and videos, AI can quickly identify critical information, reducing claim review times from weeks to mere hours and improving overall efficiency.

Another emerging trend is leveraging agentic AI systems that autonomously analyze, plan, and execute tasks within structured workflows. Unlike traditional automation, which follows fixed, rule-based processes, agentic AI adapts dynamically, makes context-aware decisions, and operates with a level of self-governance. Powered by advanced machine learning, it enhances efficiency, flexibility, and decision-making in complex environments. These systems handle domain-specific tasks like data extraction, fraud detection, anomaly identification, and decision support. Serving as the intelligence layer of the workflow, they enhance efficiency and decision-making through advanced automation.

The result is faster settlements for legitimate claims, reduced administrative costs, and an enhanced customer experience. In an industry where trust is paramount, the ability to process claims quickly without sacrificing accuracy gives insurers a significant competitive advantage.

Overcoming Implementation Challenges

While AI is a powerful tool, it isn't perfect. Despite its transformative potential, implementing AI in fraud detection and claims management comes with challenges. One of the biggest challenges in AI-driven fraud detection is the risk of false positives — legitimate claims being incorrectly flagged as fraudulent. Over-reliance on AI without human oversight can lead to frustrated policyholders, increased dispute resolution costs, and potential reputational damage.

The solution is establishing a hybrid model that blends AI automation with human expertise. AI should act as an intelligent assistant, identifying patterns, flagging anomalies, and presenting data-driven insights. However, final decisions should still involve experienced and trustworthy claims adjusters who can apply contextual judgment and verify AI-generated findings.

A hybrid approach consists of three key elements:

• AI-driven fraud detection: AI scans claims for anomalies, inconsistencies, and suspicious behavior, flagging high-risk cases for review.
• Human validation: Trained fraud investigators assess flagged claims, ensuring that legitimate cases are not wrongly denied.
• Continuous AI training: Machine learning models are regularly updated with new privacy-compliant data, allowing AI to adapt to evolving fraud tactics and reduce false positives over time.

This collaborative human-in-the-loop approach ensures insurers reap the benefits of AI's speed and scalability while maintaining fairness and accuracy in claim resolutions. It's about striking the right balance — using AI to enhance human decision-making rather than replace it entirely.

Data security is also a common concern with increased AI integration. AI-driven systems process vast amounts of sensitive information, from medical records to financial transactions. These systems are vulnerable to cyberattacks and data breaches without stringent security measures. Insurers must adopt robust encryption protocols, strict access controls, and de-identification techniques to protect customer data.

Another challenge is the risk of model drift and bias. AI models must be continuously monitored to ensure they remain accurate and fair. Bias in training data can lead to skewed decision-making, disproportionately flagging certain demographics for fraud investigation. To mitigate this risk, insurers should implement transparency measures, regularly audit AI algorithms, and use diverse datasets to train machine learning models.

Regulatory compliance is another critical consideration. As AI becomes more deeply integrated into claims processing, insurers must navigate a complex legal landscape. Compliance with industry regulations and ethical guidelines is essential to avoid potential lawsuits and maintain consumer trust. A structured AI governance framework — incorporating transparency, accountability, and ethical considerations — ensures AI adoption aligns with regulatory standards.

The Race to Automation: Why Insurers Must Act Now

Fraudsters aren't waiting. Every day, they refine their tactics, using AI to create more convincing fake claims. Insurers must move just as quickly — if not faster. The race to automation is not just about keeping up with fraud; it's about securing a future where AI-powered claims management is the norm, not the exception.

The insurance industry is at a crossroads. Companies that embrace generative AI will lead the way, while those that hesitate will struggle to keep up. The future of fraud detection and claims management isn't coming — it's already here. Insurers must decide whether they want to be proactive innovators or reactive bystanders.

Despite billions poured into insurtech over the past decade, commercial insurance agents and account managers still spend over 50% of their time toggling between fragmented systems instead of serving clients. 

The commercial insurance industry has reached an inflection point. Faced with rising customer expectations, evolving risks, and growing competitive pressures, agents and carriers are increasingly turning to technology for answers. However, the proliferation of single-purpose tools and point solutions has created a new set of challenges, hindering true transformation.

Quote comparison tools, policy review software, appetite search engines – while valuable in isolation, these point solutions have inadvertently reinforced existing data silos and workflow inefficiencies. The result is a disjointed ecosystem where agents toggle between multiple systems, manually reconcile data, and struggle to gain a holistic view of their clients and business. This fragmented ecosystem undermines productivity, causes errors, and frustrates clients seeking seamless interactions.

As the co-founder and CEO of an AI-powered platform for commercial insurance, I've seen how this fragmentation impedes growth. Agents spend countless hours on low-value tasks, carriers miss opportunities to streamline underwriting, and clients experience disjointed interactions. The future of insurance demands a different approach – one that brings together data, workflows, and intelligence into cohesive platforms designed for the industry's unique needs.

The Limitations of Point Solutions

To understand why point solutions ultimately fail commercial insurance, we must recognize the industry's inherent complexity. Insurance data spans structured and unstructured sources, from policy forms and loss runs to emails and PDFs. Workflows are deeply interdependent, with each process relying on data and decisions from multiple upstream and downstream systems.

In this environment, standalone tools create more problems than they solve. Integrations become a constant struggle, with IT teams devoting countless hours to building and maintaining brittle connections between disparate systems. Data definitions vary across solutions, leading to inconsistencies that undermine analytics and decision-making. And with each new tool, users face increased cognitive overload, navigating multiple interfaces to complete routine tasks.

Imagine an agent assessing a client's loss history, forced to toggle between emails, PDFs, and quote engines—wasting valuable hours and missing critical insights. Compounding these challenges are the industry's stringent regulatory requirements. From data privacy to audit trails, insurance demands comprehensive governance that point solutions simply can't provide on their own. This creates an illusion of digitization, masking underlying inefficiencies and compliance risks.

The Power of Platforms

The path forward lies in holistic insurance platforms that unify data, orchestrate workflows, and embed intelligence at every step. Unlike point solutions, platforms take a fundamentally different approach to value creation.

At their core, insurance platforms serve as a unified data layer, bringing together information from across the policy lifecycle into a single source of truth. By standardizing data definitions and providing comprehensive governance, platforms eliminate silos and enable seamless information sharing among brokers, carriers, and clients.

But data connectivity is just the beginning. True platforms also orchestrate workflows end-to-end, guiding users through complex processes while automating repetitive tasks behind the scenes. By embedding best practices and intelligent recommendations directly into workflows, platforms turn data into action, helping teams work smarter and faster.

Perhaps most importantly, insurance platforms leverage this unified data and workflow foundation to deploy advanced analytics and AI at scale. Rather than isolated pockets of intelligence, platforms infuse every decision with contextual insights – from identifying at-risk accounts to optimizing carrier selection. As more data flows through the platform, these insights become sharper, creating a powerful flywheel effect.

The AI Catalyst

The rise of AI simultaneously necessitates and amplifies platform adoption. Powerful generative AI tools have lowered barriers to building specialized capabilities, commoditizing point solutions. Consequently, competitive advantage shifts to platforms that integrate AI comprehensively across the insurance lifecycle.

For instance, AI-powered platforms instantly identify hidden risks by scanning emails, prior policies, and loss runs—tasks that fragmented solutions cannot accomplish efficiently. Insurers must embed AI seamlessly into their operations, leveraging unified data architectures and workflow integrations to unlock AI's full potential while mitigating associated risks.

The Path Forward

Insurance leaders must prioritize platforms over fragmented point solutions. Embracing this shift requires adopting a new mindset—valuing holistic transformation over incremental quick fixes. Brokers, carriers, and managing general agents (MGAs) who embrace a platform-first approach position themselves strategically for success in the evolving, AI-driven marketplace.

This transition requires deliberate actions:

• Begin with a comprehensive audit of existing systems to identify redundancies.
• Develop a strategic road map prioritizing early wins and long-term integration.
• Partner with technology providers experienced in insurance complexities, proven in deploying holistic platforms.
• Leverage this opportunity to significantly reduce outsourcing expenses—often by 80% or more—by automating processes traditionally handled externally.

Legacy systems must evolve, data models must be standardized, and workflows must be reimagined. Yet the rewards—significant efficiency gains, accelerated growth, and vastly improved customer experiences—will far outweigh the effort.

The insurers that will lead the next decade are those that build their businesses on robust, integrated platforms. By uniting data, workflows, and intelligence, these platforms will become the foundation for innovation and a definitive source of competitive advantage.

Your AI and automation initiatives will fail.

Not because of bad code. Not because your data scientists aren't smart enough. But because you'll lack the one thing that determines whether any AI initiative succeeds: observability.

If you can't see what your automation is doing — how it's affecting business processes, where it's breaking down, and what value it's delivering — you're flying blind. And in high-stakes domains such as distribution, new business underwriting, claims, and retention, that's a recipe for expensive failure.

The insurance industry is doubling down on AI, machine learning, and process automation. But here's the truth most don't want to hear: Implementing AI is the easy part. Proving it works — and improving it over time — is where the real challenge lies.

Automation Without Visibility Is Just Faster Failure

At Neutrinos, we work with insurers that are pushing the boundaries of intelligent automation. And we're seeing, again and again, that after the initial excitement of go-live, leaders are left asking:

• Is it actually working?
• Are we saving time, or just doing things faster without better outcomes?
• Where is human intervention still needed - and why?

These questions aren't technical — they're strategic. And they're impossible to answer without observability baked into the entire automation lifecycle.

This isn't about tracking CPU usage or memory spikes. Observability in the context of AI and process automation means real-time, contextual insight into your business metrics:

• How is the policy issuance cycle trending post-automation?
• Are underwriters accepting or overriding AI-generated decisions?
• Which customer segments are seeing improved experiences—and which aren't?

Without this visibility, AI becomes a black box. And black boxes don't earn trust — or ROI.

The Observability Trinity: Leading, Lagging, and Real-Time Indicators

To extract value from AI initiatives, insurers need to shift from retrospective reporting to proactive insight. That means tracking three types of indicators:

1. Leading Indicators – Metrics that forecast success or failure early, such as time-to-decision, document intake accuracy, or triage confidence scores.
2. Real-Time Signals – Operational insights that allow for immediate course correction, like exception frequency, process fallbacks, or system latencies.
3. Lagging Indicators – Traditional business outcomes like cost reduction, improved persistency rates, or faster policy issuance cycles.

The magic lies in correlating them. If you see triage decisions being overridden frequently (real-time), it could signal that your risk model needs retraining (leading), which if left unaddressed could result in increased underwriting time and reduced efficiency (lagging).

Observability makes this feedback loop visible — and provides automated, actionable insight.

When this visibility is fully embedded into the automation lifecycle — from initial ideation and design to deployment and continuous improvement — insurers can make intelligent, timely adjustments to improve performance by shifting observability left.

Use Case: New Business Underwriting in Life & Annuities

Life & annuities underwriting is ripe for transformation — and risk. The process involves vast amounts of unstructured data, human judgment, and regulatory complexity. That's why insurers are increasingly applying AI to:

• Automate document extraction and data enrichment
• Use natural language processing (NLP) to analyze medical records and lifestyle disclosures
• Triage applications for fast-track vs. full manual review

Sounds great. But after deployment, reality sets in: Are the right cases being fast-tracked? Are policy decisions aligned with actual risk? Are underwriters trusting the AI or working around it?

This is where observability must step in. A well-designed observability framework will monitor metrics like:

• Fast-track case approval vs. post-issue adjustment rates
• Frequency of manual intervention by underwriters
• Average time to underwrite per segment, before and after automation
• Confidence vs. override correlation for AI-generated recommendations

These aren't just performance metrics — they're trust metrics. And they directly inform whether your AI is doing what it was intended to do.

From Insight to Action: Why Observability Isn't Passive

Observability isn't just about dashboards and data. It's about decisions.

Once you have visibility into how your automation is performing, you can begin to optimize. You might adjust your triage rules. Retrain your NLP models. Refine your underwriting workflows. Or even re-segment your customer cohorts.

The point is: AI isn't static. Your observability layer shouldn't be either.

In fact, the next evolution of observability is prescriptive: platforms that not only show you what's happening but recommend what to do next. This is where proactive optimization begins — not with human guesswork but data-backed decision support.

Why to Build Observability In, Not On

Most platforms treat observability as a bolt-on — something you figure out after launch. At Neutrinos, we believe it should be core to your automation architecture.

So our automation platform includes observability capabilities that track the entire lifecycle of a process:

• From intake to triage to decision
• From AI model inference to human review
• From business rules to real-world outcomes

It's not just visibility for IT — it's insight for business leaders, compliance teams, underwriters, and customer experience (CX) strategists.

Whether it's surfacing drops in the policy journey, highlighting model drift, or comparing AI-generated recommendations to human decisions — effective observability helps insurers optimize not just automation, but outcomes.

Observability: The Real AI Differentiator

In a market where most insurers are deploying similar tools and technologies, the competitive edge won't come from your AI engine. It'll come from how well you can see, understand, and improve what it's doing.

Observability is what separates the pilot projects from the enterprise-grade transformations.

Your AI investments don't have to fail. But if you're not watching the right metrics in the right way, you'll never know if they're working — and you won't know how to fix them if they're not.

Visibility isn't optional. It's strategic. And it's the key to unlocking return on investment (ROI) from your AI initiatives.

The private credit market is witnessing a wave of strategic partnerships, and one of the most significant developments comes from TCW Group and Nippon Life Insurance. In a major move to anchor TCW's alternative credit business, Nippon Life has committed $3.25 billion in capital — marking another milestone in the convergence of insurance capital and private credit.

This deal not only reflects the growing appetite for private credit but also highlights the increasingly vital role that insurance companies are playing as strategic investors in illiquid assets. For TCW, this partnership solidifies its position as a key player in alternative credit, while Nippon Life gains access to robust returns from one of the fastest-growing asset classes.

Let's break down this deal, its significance for the broader market, and why partnerships like TCW-Nippon Life are shaping the future of private credit.

The Deal: $3.25 Billion in Anchor Capital

At the heart of this partnership lies Nippon Life's $3.25 billion capital commitment to TCW Group. The capital will serve as anchor funding for TCW's alternative credit business, allowing the asset manager to scale its private credit offerings and attract additional investors to its platform.

This isn't Nippon Life's first foray into private credit with TCW. Earlier this year, TCW joined forces with PNC Financial to launch a $2.5 billion private credit platform — a deal in which Nippon Life also participated. Nippon Life's expanded commitment underscores its confidence in TCW's ability to deploy capital effectively and generate strong risk-adjusted returns.

For TCW, Nippon Life's anchor capital provides a springboard for growth, ensuring the firm can compete with other alternative credit managers while leveraging its expertise to capitalize on opportunities in the private credit space.

Why Insurance Companies Are Betting Big on Private Credit

The TCW-Nippon Life deal is part of a broader trend: insurance companies increasingly turning to private credit as a preferred destination for their long-term capital. With traditional fixed-income investments offering diminishing yields, insurers are seeking higher returns without compromising stability. Private credit fits this profile perfectly.

Here's why insurance capital and private credit make for a winning combination:

1. Attractive Risk-Adjusted Returns: Private credit offers yields that far outstrip traditional bonds, particularly in an environment of rising interest rates. For insurance companies with long investment horizons, these returns are especially appealing.
2. Illiquidity Premium: Insurance companies are well-positioned to take advantage of illiquid investments, as their liability structures allow for long-term capital commitments. This makes private credit a natural fit.
3. Diversification Benefits: Private credit provides insurers with exposure to a diverse range of assets and industries, helping them spread risk while enhancing overall portfolio returns.
4. Strategic Partnerships: Deals like the one between TCW and Nippon Life demonstrate how insurers can partner with established asset managers to gain direct access to private credit opportunities, ensuring their capital is deployed efficiently.

TCW Group: Expanding Its Private Credit Platform

For TCW, Nippon Life's commitment represents more than just capital — it's a validation of TCW's alternative credit strategy and its ability to deliver results. As private credit continues to attract institutional interest, asset managers like TCW are scaling their platforms to meet growing demand.

The partnership with Nippon Life, alongside TCW's earlier collaboration with PNC Financial, reflects a clear strategy: leverage anchor capital to build scale and attract additional investors. By securing significant commitments from trusted partners, TCW is well-positioned to:

• Expand its deal pipeline: With $3.25 billion in anchor capital, TCW can pursue larger and more complex transactions.
• Attract additional investors: Anchor commitments provide a foundation that other institutional investors find attractive, helping TCW grow its platform further.
• Compete with larger players: As competition in the private credit space intensifies, TCW's ability to secure long-term capital commitments gives it an edge.

Private Credit: The Growing Asset Class

The TCW-Nippon Life deal is a testament to the growing importance of private credit as an asset class. What was once a niche market has evolved into a multitrillion-dollar industry, attracting capital from a diverse range of investors, including insurers, pension funds, and endowments.

Several factors are driving this growth:

• Bank Retrenchment: As traditional banks pull back from middle-market lending, private credit funds have stepped in to fill the gap.
• Higher Yields: Private credit offers higher yields compared with public fixed-income markets, making it an attractive option for yield-hungry investors.
• Flexibility and Customization: Unlike syndicated loans, private credit transactions are often tailored to meet the unique needs of borrowers, creating wins for both lenders and companies.
• Resilient Performance: Even during periods of economic uncertainty, private credit has demonstrated resilience, further solidifying its appeal.

For insurance companies like Nippon Life, this asset class represents a strategic opportunity to deploy capital in a way that aligns with their long-term investment goals while enhancing returns.

What This Means for the Market

The TCW-Nippon Life partnership highlights a broader trend: the deepening relationship between private credit managers and insurance companies. As private credit platforms scale, partnerships with insurers provide a critical source of anchor capital, allowing managers to pursue larger opportunities and compete on a global stage.

For the market, this deal signals two key shifts:

1. Private Credit's Institutionalization: The involvement of insurance giants like Nippon Life underscores private credit's evolution into a mainstream asset class.
2. The Power of Partnerships: Strategic collaborations — like TCW's partnerships with Nippon Life and PNC Financial — are becoming a hallmark of success in the private credit industry.

Final Thoughts

TCW Group's $3.25 billion capital commitment from Nippon Life is more than just another deal — it's a sign of where the private credit market is heading. As insurers seek higher returns and private credit managers look to scale, partnerships like this one are setting the stage for the next chapter in alternative credit.

For TCW, Nippon Life's commitment reinforces its growing leadership in the space. For Nippon Life, the deal represents a strategic investment in one of the most attractive asset classes available today. And for the broader market, this partnership highlights the continued evolution of private credit as a key pillar of institutional finance.

Positive outlook for life science sector

The life science sector in Latin America is experiencing significant growth, particularly in pharmaceuticals and clinical trials. With a projected compound annual growth rate of 10% in the pharmaceutical sector since 2021 and a diverse population with improving healthcare infrastructure, Latin America is a hub for clinical trials for international trial sponsors.

This growth has been driven by an aging population that requires advanced healthcare. The Latin American life science market has expanded due to increased healthcare investment, regulatory improvements, a focus on local production, and advancements in technology that have helped to broaden treatment options.

From a regulatory standpoint, Latin American countries are progressively aligning their practices with international standards, including good clinical practice (GCP) and International Council for Harmonization (ICH) guidelines. This harmonization contributes to more efficient and expedited approval processes for new pharmaceuticals and medical devices, enhancing growth opportunities for their products domestically and internationally.

Despite challenges posed by the current geopolitical environment, strong global demand remains for pharmaceutical products, providing significant opportunities for the Latin American market. As life science companies begin to export outside of Latin America, their liabilities and potential exposures change, making insurance an essential part of risk management. Not having adequate insurance in place for these businesses can mean they could be financially responsible for any injuries or damage caused by their products, which can lead to substantial legal and medical expenses.

With the rising costs of litigation and settlements globally, it's crucial for these businesses to be aware of these exposures when entering the global market. This is why working with specialist life science insurance providers is critical to ensure companies in the sector have adequate protection for their exposures, globally.

The need for environment liability cover

This section was written by Olivia Hogan, senior underwriter – international casualty, at Markel.

The financial implications of an environmental incident can be devastating, and with evolving environmental regulations, stricter laws are being implemented to improve compliance across the board.

One of the immediate impacts of more stringent environmental regulations on businesses is the necessity for compliance and a reinforced commitment to environmental responsibilities. Consequently, insurance companies are prompted to adapt their risk assessment models to address the increased environmental risks associated with these regulations, such as the probability of non-compliance or accidental breaches. The evolving landscape of environmental laws has driven innovation within the insurance sector, where insurers are now creating products designed to cover these risks on behalf of policyholders.

While numerous environmental laws and statutes exist across Latin America, they're governed by the "polluter pays" principle: "Those who produce pollution should pay for the costs associated with damage caused." Brazil, for example, is globally recognized for its vast and rich biodiversity, and protecting these natural resources is at the forefront of the country's environmental policies. The polluter pays principle is embedded in various laws and regulations in Brazil aimed at preventing and mitigating environmental damage.

Companies operating in industries where significant environmental impact could occur (e.g., mining, oil and gas) are required to take out environmental liability insurance in certain countries across the region to mitigate risk associated with their activities.

In Mexico, for example, transporters of hazardous materials are required to have environmental insurance coverage that meets the standards set by the Mexican government. These should specifically cover the costs of cleanup and remediation of environmental contamination resulting from spills or leaks. Transporters must ensure their insurance policies comply with the requirements set, which involves working with insurance providers that are familiar with the specific needs and regulations related to the transport of hazardous materials. Non-compliance with these insurance requirements can result in fines and legal penalties.

Environmental liability insurance will provide financial protection against environmental damage, statutory liability, and onsite first-party clean-up costs, as well as off-site clean-up costs and third-party bodily injury and property damage.

The demand for innovative and comprehensive coverage solutions will continue to grow as regulations tighten on an international level. Therefore, having an insurance partner that can provide comprehensive and adaptive coverage that evolves in line with legislation is paramount, ensuring businesses and individuals are fully protected if an incident occurs.

Long-term care (LTC) planning has long been one of the most complex and emotionally charged areas of financial advisory services. As the aging population grows and care costs continue to escalate, advisors and clients alike face a daunting set of challenges. Traditional planning tools often rely on broad averages and generic simulations such as Monte Carlo that fail to capture the nuances of an individual's future needs and don't motivate families to plan for LTC. However, advances in artificial intelligence (AI) are beginning to transform this landscape, offering more precise and personalized approaches to LTC planning.

The Complexity of Long-Term Care Planning

Conventional tools that tend to use national averages and basic models can lead to several recurring issues:

• Delayed Engagement: Many clients postpone LTC discussions until a crisis occurs, leaving little time to develop a thoughtful strategy.
• Impersonal Data: Generic statistics and broad-based simulations do little to illustrate the true financial impact of LTC on an individual family.
• Lost Opportunities: Without a tailored planning tool, advisors often struggle to convert early LTC discussions into concrete strategies, whether that means guiding a family toward an appropriate insurance policy or structuring a comprehensive financial plan.

These challenges highlight why LTC remains one of the few unsolved wildcard scenarios in retirement planning. Advisors and clients must contend with significant uncertainty. 

Yet, it is precisely this uncertainty that offers a last-mile opportunity for advisors to differentiate themselves by providing uniquely tailored, high-value solutions.

The AI Advantage in LTC Planning

Unlike traditional methods, AI-driven platforms can analyze a vast array of data, from regional cost variations and healthcare inflation to individual health status and family dynamics, to generate a personalized projection of a client's LTC journey. New technology solutions such as those we've developed at Waterlily are leveraging AI to personalize LTC planning, starting with streamlined intake processes.

These platforms create a tailored set of predictions to tell an insightful story of a client's future LTC needs. This narrative includes detailed projections on the timing and duration of care, anticipated costs, and even the care hours that will be taken on by family caregivers. Such personalized insights allow advisors to move beyond vague "what if" scenarios, initiating rich conversations that address the specific realities of each client's situation.

Enhancing Advisor-Client Interactions

The precision of AI-generated projections fundamentally changes how advisors engage with clients on the topic of long-term care. With clear, individualized data at hand, advisors are better positioned to:

• Initiate Rich Conversations: Instead of relying on broad averages, advisors can discuss specific care projections tailored to the client's circumstances. This not only demystifies the planning process but also helps clients understand the real implications of their choices.
• Accelerate Decision-Making: When clients are presented with a clear plan that outlines expected timelines and costs, this shortens the time from initial inquiry to concrete decisions, such as purchasing an appropriate policy or annuity.
• Unlock Premium Growth: Personalized planning helps overcome the emotional barriers that often hinder LTC discussions. By converting these conversations into high-value, concrete plans, advisors can capture opportunities that might otherwise be lost.

These capabilities tackle key challenges in traditional LTC planning by promoting early client engagement and fostering stronger, data-driven advisor relationships. Further, LTC planning isn't just about traditional LTC insurance. Innovative options like life with rider policies, hybrid solutions, annuities, and even short-term care are energizing the market and offering clients potentially more competitive choices than ever before. When used effectively, AI makes it easier to build a strategy that educates, motivates, and covers every aspect of a client's long-term care needs and wants.

Balancing Technology With the Human Touch

While AI is undeniably powerful, its greatest strength lies in complementing, not replacing, the human expertise that financial advisors bring to the table. The nuanced and emotionally charged nature of LTC planning demands empathy, active listening, and the ability to navigate complex family dynamics. AI provides the detailed, data-driven insights that can inform these discussions, but it is the advisor who translates this information into a personalized plan that aligns with the client's overall financial goals and emotional needs.

In this evolving landscape, the role of the advisor remains as crucial as ever. By integrating AI-driven insights into their practice, advisors can offer a service that not only anticipates future expenses but also supports clients through one of the most challenging aspects of retirement planning.

The Future of LTC Planning for Advisors

As we move further into the era of digital transformation, the integration of AI into LTC planning is likely to become a standard practice in the insurance brokerage community. The ability to provide detailed, personalized care projections will not only help families prepare more effectively but will also drive opportunities for advisors to convert early, meaningful discussions into robust financial strategies.

At least for our rapidly aging society on the cusp of navigating long-term care with limited funds and family support, the potential impact of AI is significant. With more accurate projections and a personalized approach, advisors can help families navigate the uncertainties of aging with confidence. By combining technological innovation with the irreplaceable human touch, the insurance brokerage community is poised to turn one of the most challenging aspects of financial planning into an engaging and ultimately more secure experience for everyone involved.

In a field where the stakes are incredibly high, leveraging AI to craft clear, personalized LTC plans can be transformative. Advisors have the opportunity to remain enduring pillars in the insurance and financial services landscape by ensuring that families are not only financially secure but also emotionally supported as they navigate the future.

Insurtech startups have sprung up like mushrooms since the Great Recession — a period when public confidence in the traditional financial sector hit rock bottom. Full-stack upstarts were seen as existential threats to their established insurance player counterparts struggling to adapt to the digital age.

Nearly a decade after the insurtech rush in the first half of the 2010s, investor interest in these challenger brands has waned. Many of these startups have had their fair share of legal troubles, revealing the cracks in their business models. Given the growth and decline of insurtechs, should they still keep traditional insurance companies up at night?

The Strengths of Traditional Insurers Are the Weaknesses of Unicorns

Rapid innovation is the calling card of insurtech startups. They exist to disrupt the status quo, identify problems and use advanced technologies to solve them.

Technologically agile competitors strike fear into the hearts of decision-makers at established insurance companies. Startups have prompted old-school senior executives to reconsider legacy thinking and move out of their comfort zones.

What insurtechs don't have going for them is operational stability. Their leaders can be serial entrepreneurs with no previous insurance background. Lemonade's Shai Wininger and Hippo's Assaf Wand are excellent examples.

Wininger founded and co-founded several tech ventures — including Trimus, Handsmart Software, Mobideo, Fiverr and Santa — before starting his crusade to revolutionize renters and homeowners insurance. Wand was the head honcho of Foris Telecom and Sabi before developing one of the first smart home insurance products.

These newcomers to the industry excel at innovation. However, they lack expertise in risk management and regulatory compliance. In contrast, incumbents shine in these areas because of their centuries of head start on their tech-driven competition.

The genesis of the American insurance industry dates back to the 18th century, and many of today's largest home insurance companies — such as State Farm, Allstate, Liberty Mutual, USAA and Farmers — are decades older than Lemonade and Hippo. Full-stack insurtech firms haven't been around the block enough times to recognize the fundamental flaws in their business models and iron out the kinks of their operational frameworks.

Poor Stock Performance — Investors Give Insurtech Enterprises a Reality Check

The turn of the 2020s was a watershed moment in the history of insurtech startups. Numerous full-stack unicorns went public between 2020 and 2021 to raise much-needed capital as they targeted profitability. Unfortunately for these newly minted publicly traded entities, the euphoria was short-lived.

Charles River Associates (CRA) compared the stock prices of 13 insurtechs against 18 traditional insurance companies that went public between 2015 and 2024. The consulting firm's two sets of findings were as different as night and day.

Most insurtech enterprises recorded negative returns against the S&P 500 (SPX) and the S&P 500 Insurance Industry Group indices three months and one year after their initial trading date. This trend persisted after examining these enterprises' historical stock prices up to April 30, 2024 — the date on which CRA compiled the data.

On average, insurtech stock prices plummeted 24% three months after their first trading date and 19% one year following their initial public offering (IPO) with or without a special purchase acquisition company (SPAC). The more time passed, the worse the drop was. From going public to April 30, 2024, 61% of the value of insurtech stocks vanished.

This phenomenon doesn't extend to the greater insurance industry. The noninsurtech stocks yielded impressive medium- and long-term returns. Their value jumped 33% one year after their initial trading date and 318% by April 30, 2024.

Many factors come into play when a stock chronically underperforms. However, no factors were as significant as getting involved in high-profile legal scandals.

Hippo Fuels Legal Arguments Against SPACs

Some insurtech unicorns used SPACs to go public for practical reasons. These blank-check companies promise fast execution, upfront price discovery, low marketing costs and quick access to operational expertise.

The problem is that SPAC mergers are subject to less scrutiny. The process requires less due diligence and can result in exaggerated valuations, which is what happened to Hippo after its ticker symbol floated on the New York Stock Exchange.

Hippo’s market capitalization was $5 billion when it went public. By September 2023, its valuation plunged by 96%, reducing its valuation to about $222 million. Mammoth withdrawals by the insurtech's SPAC investors triggered the decline.

Hippo's lackluster stock performance compelled the board to oust Wand as CEO in June 2022. The company’s stock launch also helped galvanize the SEC into imposing additional IPO-by-SPAC procedural and disclosure requirements to strengthen investor protections.

Increased oversight over SPACs should prevent future public companies from suffering the same fate as Hippo. However, the stigma of entering public markets through a SPAC has followed tech-first insurers — including those that chose the traditional IPO route.

Insurtech Firms Are Prone to Securities Class Action Litigation

The insurance industry is no stranger to lawsuits. All players — big and small — get in legal trouble on occasion. However, insurtechs account for a significant portion of securities class action litigation relative to their quantity.

Tech-first insurers are in the minority. There were 1,500 of them in 2024 — a tiny fraction of all regulated insurance companies in the U.S. This figure is only slightly higher than the average number of licensed foreign insurers per state at 1,403, according to the National Association of Insurance Commissioners.

According to CRA's insights, there were 39 Section 10(b) and 12 Section 11 filings in the insurance industry from 2015 to 2024. Nine insurtech firms that went public in the same period found themselves in hot water by getting involved in these cases.

One of them was Clover Health. Investors sued this Jersey City, N.J., insurtech for allegedly hiding the fact that it was under active investigation by the Department of Justice for undisclosed third-party deals, kickbacks and many other issues when it went public.

The company also allegedly misrepresented its revenue streams and overstated its software platform’s capabilities. The allegations ended in a settlement worth $22 million without Clover Health admitting wrongdoing.

Another widely discussed example was GoHealth. The complainant alleged that the Chicago-based health insurance marketplace's IPO registration statement failed to contain crucial information for investors — like the higher risk of customer churn due to its limited carrier base and unique business model.

GoHealth and the plaintiffs settled the case for $29.3 million after more than three years of litigation.

Most of these securities class action lawsuits involved accusations related to the fundamentals of insurance operations. They highlight insurtechs' lack of operational resilience when leveraging technological innovation.

Traditional Insurance Companies Are Not Out of the Woods Yet

After many years of poor stock performance and legal battles, tech-driven startups are once again showing signs of life. They completed 71 deals in 2024’s first half despite reduced funding, crowning the U.S. as the leading insurtech market globally.

Furthermore, insurtechs that went public from 2020 onward rebounded in 2023. Their stocks collectively went up by 22%, and many of them were able to shrink their net losses. Such performance indicates that public market investors are slowly regaining their faith in insurtechs.

It would be premature to describe recent developments as a resurgence of trust in these startups, as many of them are light years away from their original valuations. Still, there's no denying that more investors are giving insurtech stocks a second look. Stock market participants may have had a change of heart because they're now more familiar with how these businesses work.

Understanding the complexities of insurtechs is vital in assessing their value as enterprises. More investors appreciate the innovations they bring to the table — like the use of telematics to collect and analyze fleet vehicle data for risk assessment or the application of AI to empower customers to complete applications and file claim reports independently with chatbots.

Hippo CEO Rick McCathron can attest to this. He said that his organization missed out on adequate analyst coverage because of its SPAC history. McCathron and Co. moved mountains to get analysts to care and are now reaping the fruits of their labor.

Hippo gained momentum in 2023 and carried it into the following year. 2024 was its best year so far; it recorded net income of $44 million in Q4. Only time will tell if Hippo can sustain this upward trajectory and if other insurtechs will report similar net earnings.

Viewing Disruptors as Collaborators Instead of Competitors

Traditional insurers shouldn't feel threatened by the recovery of insurtech stocks. It's normal for legacy-minded professionals to feel insecure about losing market share to more tech-driven players. However, established insurance companies should accept that they're here to stay and use these innovative individuals to modernize their systems, offer personalized services to customers and more.

After all, not all insurtechs are competitors. The others are partners looking for mutually beneficial collaborations to improve the insurance industry.

2025 has been a wake-up call for insurance. The demand to invest in new technology and deliver next-gen capabilities is placing insurers at a crossroads of rethinking their strategies and priorities in order to stay ahead and compete. Read Majesco’s latest thought leadership report to understand what’s needed to fuel optimization, transformation and innovation.

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Sponsored by ITL Partner: Majesco

Generative AI (gen AI) is transforming how we work and conduct business, and its impact is particularly evident in the insurance industry. From improving customer interactions to driving process efficiency and supporting decision-making, the technology is reshaping the entire value chain.

With gen AI's applications and use cases continuing to expand, our recent C-suite survey reflects a surge in optimism and confidence in the technology among insurers. 82% of insurance decision-makers now see greater potential for business impact from gen AI, based on their experience with the technology over the past year. Furthermore, 84% of leaders expect their organizations to increase their gen AI investment in 2025 as compared with 2024.

For insurers, this is an opportunity to capitalize on the immense potential that will come with scaling this technology to drive long-term growth. However, there are several key requisites that must be addressed to ensure a successful and responsible integration when designing a gen AI strategy:

Lead with value

Although gen AI has the potential to affect the full value chain, we see the greatest potential in underwriting/distribution and claims.

Research shows that 40% of the average underwriter’s time is spent on administrative and other non-core tasks. These demands and the surges in submissions in turn lead to an increase in workload without a proportional increase in revenue. Through automation and task augmentation, gen AI can help underwriters handle more tasks, work more efficiently, reach better decisions faster and win more business. For example, we worked with QBE, a multinational insurance company, to scale industry-leading AI-powered underwriting solutions across multiple regions and lines of business. They are now able to make faster, more accurate business decisions and greatly accelerate market response time. Early results also indicate an increase in both quote-to-bind rate and premium.

Gen AI can significantly enhance claims processing and outcomes, whether for frequency or severity claims. Additionally, using gen AI in claims can also improve rating and pricing activities. As a best practice, carriers can incorporate learnings extracted from unstructured claims data into a feedback loop for underwriting to guide future decisions, guidelines and appetite.

Reinvent talent and ways of working

Job displacement is a common concern when discussing gen AI. However, in the insurance industry, gen AI is more likely to augment, not replace, human activity. Regulation and licensing requirements need licensed professionals to make and communicate decisions. Unless requirements change, these roles cannot be replaced by AI.

In fact, both automation and augmentation with gen AI will create daily benefits for workers. Research shows that 29% of working hours in the insurance industry can be automated by gen AI, relieving workers of many of their more mundane and tedious tasks. What's more, 36% of working hours can be augmented by gen AI, which is crucial as the industry faces staffing shortages due to an aging workforce and competition for talent.

Close the gap on responsible AI

Insurers hold a position of trust when storing and processing sensitive data belonging to customers and partners, and it's important that this trust is maintained as gen AI becomes more integrated into operations.

As gen AI becomes more autonomous, the need for responsible AI practices becomes a necessity. Insurers must implement systematic testing and monitoring across quantitative and qualitative dimensions to manage risk with the highest ethical standards. This includes controls for data privacy, cybersecurity and sustainability, to ensure compliance as regulatory requirements inevitably increase.

Quantifiable measures help demonstrate the insurer's due diligence amid escalating cyber threats. Qualitative controls are equally important, improving transparency, explainability, accuracy, and safety. Insurance products can be hard for many customers to understand, and these issues are harder to navigate in communities where past discriminatory practices have undermined industry trust.

Build an AI-enabled, secure digital core

To fully realize the potential of gen AI, insurers require a strong digital core and a secure cloud. This starts with a simplified cloud infrastructure that integrates with core systems and can support the data and model needs of AI. A continuum control plane can serve as a unified command center, orchestrating infrastructure, applications, data, network, people and processes and simplifying cloud integration across a range of vendors. This not only improves operational resilience but also enhances visibility across the enterprise and can address complexities associated with moving operations to the cloud.

Security is another critical component essential to operational resilience and data protection. As the threat landscape evolves, insurers must implement systems that reduce the risk of breaches and adopt post-quantum encryption methods to protect vital and sensitive information. A modernized data platform, leveraging technologies like vectorDBs and knowledge graphs, can help insurers make the most of their data while ensuring compliance and privacy.

Foundation models can be easily integrated with the primary cloud setup. However, as needs become more complex, it's important to reassess priorities and retrain or build a new model to address specific goals and market realities. A model switchboard allows for dynamic adjustments to models based on the weight assigned to various priorities, such as accuracy, efficiency and cost.

The AI and gen AI capabilities of core insurance platforms are evolving quickly. For example, we’ve embedded AI and gen AI throughout our Accenture Life Insurance and Annuity Platform (ALIP) with cloud-managed services that include an AI-led user experience with conversational AI navigation and intelligent alerts.

Embrace change and continuous reinvention

Many insurers are now seeing material economic gains as they scale their AI and gen AI investments for continuous reinvention. This involves disciplined replication and re-use of gen AI solutions. Multiple lines of business in claims or multiple products in underwriting may be able to use the same user interface (UI) and user experience (UX) for gen AI implementations. Investments in UI/UX, front-end and back-end coding, rule and prompt libraries and data modernization can often be leveraged across the value chain.

Insurers are accelerating their reinvention journey with gen AI. They are building a culture and capability for continuous reinvention by centering every function in the value chain around a modern digital core. As such, the future of insurance will be led by those companies that can seamlessly blend human expertise with this technology, redefining what it means to be a trusted and innovative insurer.

Insurance customers everywhere, across all lines of business, are clamoring for change. They want more simplicity, transparency and usability and lower rates. In response, regulatory solutions are being proposed in state capitals, and legislative solutions are being discussed and even hotly debated in Washington, D.C.

Meanwhile, Nvidia's annual GTC conference recently wrapped in San Jose, Calif. What started as a niche gathering of hard-core gaming enthusiasts (GTC stands for GPU Technology Conference, and GPU stands for graphics processing unit) has morphed into "The Super Bowl of AI," with a sold-out crowd of over 25,000 people attending in person and thousands more online.

What sets GTC apart from other tech conferences, and why I'm writing about it, is the growing number of non-IT participants. As AI is making English the programming language of the future, the business domain is becoming the language of AI.

This year, we learned that Nvidia's new Blackwell GPU chip is about 40x faster on half the power consumption than their existing Hopper GPU. Nvidia's next-generation GPU releases, Rubin in 2026 and Feynman in 2027, will at least be 40x faster on half the power than their predecessors. Over 90% of AI workloads run on Nvidia GPUs, and that won't change anytime soon.

We learned Nvidia is releasing an open-source operating system, called Dynamo, to orchestrate enterprise-scale GPU workloads. They also announced radical advancements in silicon photonics to accelerate switching in large-scale AI data centers. Add it all up, and Nvidia is moving beyond building AI chips to building "AI factories."

This means the latest language models with a trillion parameters will soon be outpaced by even more powerful systems, with tens of trillions of parameters on the same power consumption at a constant price.

New AI systems won't just be faster, they'll be ever smarter, more adaptable, and capable of handling complex tasks with high precision at massive volumes. This is where insurance comes in.

Imagine a world where the 2,000-odd insurers in the U.S. no longer need their own underwriting, policy administration, or claims processing units. Instead, AI-powered "super processors" handle the heavy lifting, leaving insurers to focus on marketing, finance and investment strategies. Think Visa and Mastercard. Banks once managed their own card networks, but now they outsource everything to these super-processors maintaining only affinity commercial relationships.

On the upside, insurance customers would win with lower premiums thanks to scale efficiencies and reduced overhead. Super-processors would wield unprecedented purchasing power with vendors such as auto body shops and home repair companies, further reducing costs. Litigation would logically be a fraction of what it is today with better information on all sides of a claim, harvesting and processing telematics data at machine speed in real time. Customer satisfaction would improve, perhaps dramatically. Carriers would win with variable, consumption-based pricing.

On the downside, thousands of insurance-processing jobs would disappear. But is that a problem or a solution? We know older insurance workers are retiring in numbers; we also know younger people with choices aren't flocking to the insurance industry. Attracting one talented human to run a digital workforce of 20 bots might be easier than attracting 20 humans to do mundane, bot-like work.

Might the critical path to optimal simplicity, transparency, usability, and lower premiums in insurance be digital black-box agents hosted in insanely complex, massively expensive AI data centers? And might this paradoxical solution be closer than we think?